Spark Cosine Similarity (DIMSUM algorithm ) sparse input file

Question

I was wondering whether it would be possible for Spark Cosine Similarity to work with Sparse input data? I have seen examples wherein the input consists of lines of space-separated features of the form:

id feat1 feat2 feat3 ...

but I have an inherently sparse, implicit feedback setting and would like to have input in the form:

id1 feat1:1 feat5:1 feat10:1
id2 feat3:1 feat5:1 ..
...

I would like to make use of the sparsity to improve the calculation. Also ultimately I wish to use the DIMSUM algorithm for calculating all-pairs-similarity that has been recently incorporated into Spark. Could someone suggest a sparse-input format that would work with DIMSUM on spark? I checked the example code and in the comments it says "The input must be a dense matrix" but this code was in examples so I don't know whether it was referring only to one particular case.

spark/examples/src/main/scala/org/apache/spark/examples/mllib/CosineSimilarity.scala

That's the path to the example code that I'm referring to.

Just a couple of lines representing how the sparse-input format should look (from a recommendation system perspective, user_id feat1:1 feat2:1 ...), to work with cosine similarity, would be extremely helpful.

Also would it be okay if I left the user_ids as strings?

I am aware that libsvm format is similar but there is no notion of a user id in this case, only input instances with features so I was wondering how the libsvm format would translate into a recommendation system domain?

My apologies for the extremely simplistic questions, I am extremely new to Spark and am just getting my feet wet.

Any help would be much appreciated. Thanks in advance!

score 2 · Answer 1 · answered Jun 23 '15 at 04:09

Why not? Naive solution can look more or less like this:

// Parse input line
def parseLine(line: String) = {
    def parseFeature(feature: String) = {
        feature.split(":") match {
            case Array(k, v) => (k, v.toDouble)
        }
    }

    val bits = line.split(" ")
    val id = bits.head
    val features = bits.tail.map(parseFeature).toMap
    (id, features)
}

// Compute dot product between to dicts
def dotProduct(x: Map[String, Double], y: Map[String, Double]): Double = ???

// Compute norm of dict
def norm(x: Map[String, Double]): Double = ???

// Compute cosine similarity
def sparseCosine(x: Map[String, Double], y: Map[String, Double]): Double = {
    dotProduct(x, y) / (norm(x) * norm(y))
}

// Parse input lines
val parsed  = sc.textFile("features.txt").map(parseLine)

// Find unique pairs
val pairs = parsed.cartesian(parsed).filter(x => x._1._1 != x._2._1)

// Compute cosine similarity between pairs
pairs.map { case ((k1, m1), (k2, m2)) => ((k1, k2), sparseCosine(m1, m2)) }

Spark Cosine Similarity (DIMSUM algorithm ) sparse input file

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